Modeling the Longitudinality of User Acceptance of Technology with an Evidence-Adaptive Clinical Decision Support System

This paper presents multiple innovations associated with an electronic health record system developed to support evidence-based medicine practice, and highlights a new construct, based on the technology acceptance model, to explain end users’ acceptance of this technology through a lens of continuous behavioral adaptation and change. We show that this new conceptualization of technology acceptance reveals a richer level of detail of the developmental course whereby individuals adjust their behavior gradually to assimilate technology use. We also show that traditional models such as technology acceptance model (TAM) are not capable of delineating this longitudinal behavioral development process. Our TAM-derived analysis provides lens through which we summarize the significance of this project to research and practice. We show that our application is an excellent exemplar of the “end-to-end” IS design realization process; it has drawn upon multiple disciplines to formulate and solve challenges in medical knowledge engineering, just-in-time provisioning of computerized decision-support advice, diffusion of innovation and individual users’ technology acceptance, usability of human-machine interfaces in healthcare, and sociotechnical issues associated with integrating IT applications into a patient care delivery environment

CAGE - Consensus Algorithm Genetically Encouraged

Blockchain Technology has gone beyond just cryptocurrency. There is a growing need not only for development in blockchain technology to support the needs of Web 3.0, but also a need for research into Blockchain Technology. One of the fundamental components of Blockchain Technology is the consensus algorithms used to i) select a node in the network responsible for providing a block added to the blockchain; and, ii) verify that block and ensure trust between the nodes within the system. This research proposes a newly developed consensus algorithm for Blockchain Technology. This research draws on inspiration from nature and the field of evolutionary computation, and selection methods in particular. The selection method is a mixture of Darwinianism and Fatigue-based systems, used in many evolutionary algorithms. This selection method is applied successfully as a consensus algorithm in a Blockchain Technology Systems. The proposed consensus algorithm is called Consensus Algorithm Genetically Encouraged, or CAGE for short. An experimental framework was developed in which to test CAGE fairly. In this experimental framework CAGE was then tested and compared to another similar consensus algorithm, Proof-of-Elapsed-Time (PoET), many times. Results and analysis show that as the number of nodes in a blochchain technology increase, CAGE becomes more efficient in latency and throughput of block production. Analysis showed that the node distribution of CAGE was not as even as PoET. Some modifications to the algorithm were made and the tests re-run. This proved more successful and improved the distribution of node selection whilst having no effect on throughput and latency. There are some reasons why CAGE outperforms PoET, which are mentioned in the analysis and results chapters. In summary, this research developed a newly proposed consensus algorithm, CAGE, inspired by the selection methods used in evolutionary computation. CAGE was then tested many times and results show that as the number of nodes in the blockchain technology system increases CAGE outperforms PoET in terms of latency and throughput

Academic development to support pedagogically-informed uses of learning technologies

Learning technologies are increasingly common in higher education institutions, but academics are frequently unsure how best to use these. Staff development activities focussed on technology skills are not sufficient for academics to design sound technology-based educational experiences. This research study explores this problem, seeking to increase understanding on how academic developers can support academics to make pedagogically-informed uses of learning technologies. An exploratory case study methodology was used for this 44-month research study. The data collection included class teaching observations, document analysis, semi-structured interviews and forum postings during a professional development (PD) course. The first phase of research involved the development and testing of a class teaching observation schedule, to understand current practice. The second phase of research included class teaching observations and interviews with participating academics to identify their learning needs. These research activities informed the design, development and delivery of the first part of a PD course. The final phase of research involved (a) interviews to understand the participants’ experience of the first part of the course and to identify their expectations for the remaining part of the course and (b) the delivery of the remaining part of the PD course. A thematic analysis of the participants’ forum posts and mid-course interviews led to the identification of five themes. The main contributions of this research study are related to (a) the process of academic development for learning technology use, and (b) the process of studying academic development. This study shows how the teaching development of academics can be addressed through flexible and just-in time academic development, and engaging academics in activities related to their teaching context. The student experience of technology-based teaching, the course learning resources and activities, the facilitator’s guidance, the diversity of participants’ experiences and peer discussions support academics to develop pedagogically-informed positions on teaching and learning technologies. Methodologically, the thesis suggests that researchers should use a diversity of data collection tools to gather and analyse evidence about academic development

Irrational choice and the value of information

Irrational decision making in humans and other species challenges the use of optimality in behavioural biology. Here we show that such observations are in fact powerful tools to understand the adaptive significance of behavioural mechanisms. We presented starlings choices between probabilistic alternatives, receiving or not information about forthcoming, delayed outcomes after their choices. Subjects could not use this information to alter the outcomes. Paradoxically, outcome information induced loss-causing preference for the lower probability option. The effect depended on time under uncertainty: information given just after each choice caused strong preference for lower probability, but information just before the outcome did not. A foraging analysis shows that these preferences would maximize gains if post-choice information were usable, as when predators abandon a chase when sure of the prey escaping. Our study illustrates how experimentally induced irrational behaviour supports rather than weakens the evolutionary optimality approach to animal behaviour.Financial support was received from the UK Biotechnology and Biological Sciences Research Council Grant BB/G007144/1 (to AK). MV was funded by the Investigator Grant IF/01624/2013 and Grant IF/01624/2013/CP1158/CT0012 both from the Portuguese Foundation for Science and Technology (FCT). TM was supported by a doctoral grant from FCT and a Pembroke College Graduate Scholarship. We are grateful to Eva Abraham and Vivien Ngo for their help conducting the experiments

Monitoring biological wastewater treatment processes: Recent advances in spectroscopy applications

Biological processes based on aerobic and anaerobic technologies have been continuously developed to wastewater treatment and are currently routinely employed to reduce the contaminants discharge levels in the environment. However, most methodologies commonly applied for monitoring key parameters are labor intensive, time-consuming and just provide a snapshot of the process. Thus, spectroscopy applications in biological processes are, nowadays, considered a rapid and effective alternative technology for real-time monitoring though still lacking implementation in full-scale plants. In this review, the application of spectroscopic techniques to aerobic and anaerobic systems is addressed focusing on UV--Vis, infrared, and fluorescence spectroscopy. Furthermore, chemometric techniques, valuable tools to extract the relevant data, are also referred. To that effect, a detailed analysis is performed for aerobic and anaerobic systems to summarize the findings that have been obtained since 2000. Future prospects for the application of spectroscopic techniques in biological wastewater treatment processes are further discussed.The authors thank the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit, COMPETE 2020 (POCI-01-0145-FEDER-006684) and the project RECI/BBB-EBI/0179/2012 (FCOMP-01-0124-FEDER-027462) and BioTecNorte operation (NORTE-01-0145-FEDER-000004) funded by the European Regional Development Fund under the scope of Norte2020 - Programa Operacional Regional do Norte. The authors also acknowledge the financial support to Daniela P. Mesquita and Cristina Quintelas through the postdoctoral Grants (SFRH/BPD/82558/2011 and SFRH/BPD/101338/2014) provided by FCT - Portugal.info:eu-repo/semantics/publishedVersio